Performance of genetic programming to extract the trend in noisy data series

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Borrelli:2006:PhysicaA,

• author = "A. Borrelli and I. {De Falco} and A. {Della Cioppa} and M. Nicodemi and G. Trautteur",
• title = "Performance of genetic programming to extract the trend in noisy data series",
• journal = "Physica A: Statistical and Theoretical Physics",
• year = "2006",
• volume = "370",
• number = "1",
• pages = "104--108",
• month = "1 " # oct,
• note = "Econophysics Colloquium - Proceedings of the International Conference {"}Econophysics Colloquium{"}",
• keywords = "genetic algorithms, genetic programming, Multiobjective genetic programming, Stochastic time series",
• DOI = "doi:10.1016/j.physa.2006.04.025",
• abstract = "In this paper an approach based on genetic programming for forecasting stochastic time series is outlined. To obtain a suitable test-bed some well-known time series are dressed with noise. The GP approach is endowed with a multiobjective scheme relying on statistical properties of the faced series, i.e., on their momenta. Finally, the method is applied to the MIB30 Index series.",

}

Genetic Programming entries for Antonio Borrelli Ivanoe De Falco Antonio Della Cioppa Mario Nicodemi G Trautteur

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